Solving the Fixed Charge Transportation Problem by New Heuristic Approach

The fixed charge transportation problem (FCTP) is a deployment of the classical transportation problem in which a fixed cost is incurred, independent of the amount transported, along with a variable cost that is proportional to the amount shipped. Since the problem is considered as an NP-hard, the computational time grows exponentially as the size of the problem increases. In this paper, we propose a new heuristic along with well-known metaheuristic like Genetic algorithm (GA), simulated annealing (SA) and recently developed one, Keshtel algorithm (KA) to solve the FCTP. Contrary to previous works, we develop a simple and strong heuristic according to the nature of the problem and compare the result with metaheuristics. In addition, since the researchers recently used the priority-based representation to encode the transportation graphs and achieved very good results, we consider this representation in metaheuristics and compare the results with the proposed heuristic. Furthermore, we apply the Taguchi experimental design method to set the proper values of algorithms in order to improve their performances. Finally, computational results of heuristic and metaheuristics with different encoding approaches, both in terms of the solution quality and computation time, are studied in different problem sizes

An Allocation-Routing Optimization Model for Integrated Solid Waste Management

Integrated smart waste management (ISWM) is an innovative and technologically advanced approach to managing and collecting waste. It is based on the Internet of Things (IoT) technology, a network of interconnected devices that communicate and exchange data. The data collected from IoT devices helps municipalities to optimize their waste management operations. They can use the information to schedule waste collections more efficiently and plan their routes accordingly. In this study, we consider an ISWM framework for the collection, recycling, and recovery steps to improve the performance of the waste system. Since ISWM typically involves the collaboration of various stakeholders and is affected by different sources of uncertainty, a novel multi-objective model is proposed to maximize the probabilistic profit of the network while minimizing the total travel time and transportation costs. In the proposed model, the chance-constrained programming approach is applied to deal with the profit uncertainty gained from waste recycling and recovery activities. Furthermore, some of the most proficient multi-objective meta-heuristic algorithms are applied to address the complexity of the problem. For optimal adjustment of parameter values, the Taguchi parameter design method is utilized to improve the performance of the proposed optimization algorithm. Finally, the most reliable algorithm is determined based on the Best Worst Method (BWM)

Creating Shared Value and Strategic Corporate Social Responsibility through Outsourcing within Supply Chain Management

One way to develop local clusters is to strengthen those clusters by using outsourcing to conduct strategic social responsibility, or in other words, to create shared value, which is a win-win strategy for the buyer, supplier, and society and the best and most viable alternative to traditional corporate social responsibilities. In the leading research, a model for decision-making within the supply chain has been developed for purchasing based on shared value creation, long-term relationship management, and purchasing strategies. The research consists of two strategic mathematical models, using goal programming, and then is solved by a meta-heuristic algorithm. Potential outsourcing companies are assessed and then clustered according to their geographic locations in the decision-making process. One (or several) cluster(s) was selected among clusters based on knowledge and relationship criteria. Besides, in the primary mathematical model, the orders in different periods and the selection of suppliers are determined. In this model, in addition to optimizing the cost, the dispersion of purchases from suppliers is maximized to increase relationships and strengthen all members of the cluster. Maximizing the distribution by converting a secondary objective function to goal-programming variables transforms the multi-objective model into a single-objective model. In addition to economic benefits for buyers and suppliers, this purchasing plan concentrates on strengthening the local industrial cluster, fostering employment and ease of recruitment for human resources, accessing more infrastructures and technical support facilities, developing an education system in the region, and assisting knowledge-based enterprises with development

Integrated Air Transportation and Production Scheduling Problem with Fuzzy Consideration

Nowadays, several methods in production management mainly focus on the different partners of supply chain management. In real world, the capacity of planes is limited. In addition, the recent decade has seen the rapid development of controlling the uncertainty in the production scheduling configurations along with proposing novel solution approaches. This paper proposes a new mathematical model via strong recent meta-heuristics planning. This study firstly develops and coordinates the integrated air transportation and production scheduling problem with time windows and due date time in Fuzzy environment to minimize the total cost. Since the problem is NP-hard, we use four meta-heuristics along with some new procedures and operators to solve the problem. The algorithms are divided into two groups: traditional and recent ones. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as traditional algorithms, also Keshtel Algorithm (KA) and Virus Colony Search (VCS) as the recent ones are utilized in this study. In addition, by using Taguchi experimental design, the algorithm parameters are tuned. Besides, to study the behavior of the algorithms, different problem sizes are generated and the results are compared and discussed

Identification and Ranking the Risks of Green Building Projects Using the Hybrid SWARA-COPRAS Approach: (The Case: Amol County)

Green building has attracted widespread attention in recent years. Increasing building projects in Iran has had significant environmental impacts and green building implementation is an appropriate approach to reduce the environmental risks. Green building projects facing with risks unwittingly which, reduce their efficiency. Therefore, identifying and ranking the risks can play a significant role in the success of green building projects. Due to the few studies in this area in Iran, this study aims to provides a new comprehensive framework of all the criteria and risks of green building projects. For this purpose, the first step aims to identify and screen the risks from the viewpoint of experts in green building projects and introducing the risk assessment criteria. After that, the next step is to apply the SWARA method to obtain the weight of the criteria. Finally, Ranking of the risks of green building projects has been done using the COPRAS method. This is the first attempt to solve a green building project using a hybrid of SWARA and COPRAS through a case study (Amol). The results show that risks of: the low quality of materials and equipment, the stakeholder resistance to approve the green ideas and lack of realistic goals are very important. The proposed framework can help stakeholders of green building projects in developing countries to manage project risks more efficiently

Industry 4.0 in Waste Management: An Integrated IoT-Based Approach for Facility Location and Green Vehicle Routing

The increasing production of solid waste rate in urban areas plays a critical role in sustainable development. To mitigate the adverse effects of waste and enhance waste management efficiency, this paper introduces a holistic approach that notably reduces the overall cost while mitigating social and environmental impacts. Central to the system's efficacy is the critical process of waste sorting, which enhances the output value of the waste management system. While previous studies have not extensively addressed simultaneous waste collection and sorting, this paper provides an innovative integrated framework. This approach Integrates waste collection with various bins, followed by their transfer to separation centers. At these centers, waste is categorized into organic and non-organic varieties, which are then dispatched to a recovery center at the second level. In the context of optimizing the routes at both levels, this paper presents a green, multi-objective location-allocation model. This model is designed to optimize the number and location of separation center facilities. Since the routing problem is influenced by the facility location model, it is addressed as a multi-depot green vehicle routing problem, integrating real-time information from IoT-equipped bins. This paper also proposes the vehicle routing problem with a split pickup, aiming to minimize cost, CO2 emissions, and visual pollution. The mathematical models introduced to formulate the problem are solved using the GAMS optimization software to apply an exact method, while Social Engineering Optimization and Keshtel algorithms are deployed to solve the routing problem. The proposed approach offers a comprehensive and sustainable solution to waste management, filling crucial gaps in current research and practice

Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts

In sustainable waste supply chains, selecting recycling partners is an important factor in the decision-making process. Waste supply chains have undergone many fundamental modifications because of the rise of concepts such as sustainability, circular economy, and resilience. To overcome the current shortcomings of the literature on recycling partner selection only based on sustainability aspects, an evaluation framework is developed to address recycling partner selection by considering both sustainability and resilience factors. Although developing a sustainable and resilient evaluation framework improves the process of selecting recycling partners, the problem becomes very complex, and multidimensional decision-makers require reliable and accurate tools to make informed decisions. Multi-criteria decision-making (MCDM) methods are useful decision-making tools with high reliability to address problems under uncertainty. Although previous studies have developed several MCDM methods based on various uncertainty sets, the capability to support efficient and accurate group decision-making by decision-makers’ opinions and experts’ judgments has been a major disadvantage. Therefore, this study develops a novel decision-making approach using Z-numbers based on the best-worst method (Z-BWM) and a combined compromise solution (Z-CoCoSo). The proposed novel approach for addressing a sustainability and resilience management problem in an urban setting is demonstrated in a real-life case study using Tabriz, Iran as a case study. According to the results, net profit and the robustness of the waste supply chain are the most important criteria

A dynamic approach for the multi-compartment vehicle routing problem in waste management

Urban areas worldwide face a significant environmental challenge which is increasing municipal solid waste rate. Addressing its negative consequences necessitates advancements in waste management systems. Although the previous research focused on the static routing approach in the collection phase, this paper adds a dynamic municipal solid waste collection scheme to optimize vehicle routing, accounting for fluctuations in waste generation and changes in transportation systems. This study employs, for the first time, the application of a discrete choice model (DCM) to streamline the process of re-optimization in dynamic vehicle routing problems (DVRP). At each decision epoch, DCM is applied to determine the likelihood of choosing the next geographical zone to visit bins based on current waste generation levels and traveling costs. Moreover, the multi-compartment vehicles are considered to preserve waste segregation during transportation, thereby increasing operational efficiency and regulatory compliance. Another contribution of this paper is to determine visiting priority for each bin by adjusting the time window based on the threshold waste level. Hence, this paper proposes a framework for sustainable, efficient, and effective waste management practices by integrating the benefits of dynamic and multi-compartment routing. Furthermore, a hybrid Genetic and Particle Swarm Optimization algorithm has been designed to find the best solution for the studied problem as well as some of the latest and most proficient metaheuristic algorithms. Finally, the Best Worst Method is applied to find the best-proposed algorithm to solve the presented problem, indicating that the hybrid algorithm has the highest performance in providing high-quality route plans